Research on joint optimization model and algorithm of multi‐area generation and reserve with considering its availability

Abstract

The reserve sharing amongst the interconnected power systems will facilitate the improvements on their anti‐disturbance capability and operating economy. Based on the design of reserve sharing mechanism across different areas, a multi‐area generation‐reserve sharing model is constructed. The stochastic fluctuation of load and wind power will cause the power grid to be in an unpredictable random state when the reserve is needed, and the transmission capacity constraint of the power grid may restrict the effective deployment of the reserve. To address these issues, a max‐min bi‐level optimization model is proposed to check the reserve availability. The joint generation and reserve optimization sub‐model, together with the reserve check sub‐model, constitutes the two‐stage robust optimization model in this paper. The column‐and‐constraint generation (C&CG), algorithm is adopted to solve the proposed model. The modified IEEE 118‐node test case is used to verify the proposed model and algorithm. The test results indicate that the proposed method can realize the multi‐area generation‐reserve joint optimization under the condition of guaranteeing the reserve availability, and it possesses the advantage of high efficiency in dealing with stochastic scenarios.

Economic Dispatch of Multi Regional Power Systems Based on CMOPSO Algorithm

“…Scholars such as Xu D have designed a maximum minimum two-layer optimization model and a two-stage robust optimization model to solve the problem of unpredictability during power grid backup, and used column constraint generation algorithm to solve it. This model can effectively solve the problem of unpredictability during power grid backup, and has obvious advantages in random scenarios [11]. Wang S et al designed a multi-agent power grid control scheme to meet the requirements of the system and computing platform for autonomous control of the power grid.…”

Power Side Risk Assessment of Multi-Energy Microgrids Considering Risk Propagation between Interconnected Energy Networks

“…In the microgrid, power loads can be supplied by power import and power converted from gas, hydrogen, and heat. Based on the energy conversion models discussed above, for the power side, the system level energy conversion model can be formulated as in Equation (6).…”

“…Reference [5] proposed a probabilistic multi-energy flow model focusing on power-heat-gas coupled integrated energy systems by introducing the semiinvariant method, and on this basis, an assessment method accompanying with multiple risk indicators under normal operation is proposed. Reference [6], focusing on the multiregional interconnected power grid, built a two-stage joint optimization model of power generation and reserve considering operational risks. Reference [7] analyzed the challenge faced by traditional risk assessment indices and proposed a risk assessment method from the aspects of bus voltage, network loss, and branch flow.…”

A safety check method to maximize the effective reserve by optimizing the power of the tie-line in the power market

“…However, the method provided in this paper will not only affect the normal clearing process but also improve the effective reserve resources, which not only ensures the safety of the power grid but also improves the economy of power system dispatching. For the new power system with a high proportion of new energy, compared with the Frontiers in Energy Research frontiersin.org probabilistic evaluation method (Chen et al, 2022;Liu et al, 2023) and random optimization (Xu et al, 2023), the method provided in this paper has better real-time performance and certainty, can meet the actual power scheduling requirements, and can be directly applied to the engineering algorithm of power spot market clearing.…”

“…In addition, due to changes in boundary conditions, the day-ahead clearing results may not meet the demand for an effective reserve of the system in the real-time market, especially in the new power system, where the effect of the new energy forecast is particularly significant. In the new power system, relevant research on the difficulty of reserve evaluation caused by the deviation of new energy prediction includes the probability evaluation method (Chen et al, 2022;Liu et al, 2023), random optimization (Xu et al, 2023), and robust optimization (Ran et al, 2022;Wang et al, 2022). The probabilistic evaluation method and stochastic optimization method are too dependent on the probabilistic accuracy of boundary data prediction in the day-ahead electricity spot market and cannot meet the actual scheduling demand in the day-ahead and real-time electricity spot market.…”